Systems and methods for contextual transformation of analytical model of iot edge devices

ABSTRACT

Disclosed are methods, systems, and non-transitory computer-readable medium for a contextual transformation of an analytical model for an industrial internet of things (IIoT) edge node. For instance, the method may include receiving the analytical model from a cloud service; obtaining local data of the IIoT edge node; analyzing the local data to determine a situational context of the IIoT edge node; determining whether to transform the analytical model based on a fit between the analytical model and the situational context; and in response to determining to transform the analytical model, transforming the analytical model based on the situational context to derive a transformed analytical model.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally to anedge (e.g., edge nodes) of an industrial internet of things (IIoT)system and, more particularly, to systems and methods for contextualtransformation of an analytical model at the edge.

BACKGROUND

Normally, an edge platform of an IIoT system contains a fixed set ofapplications, including specific analytics. Before deploying an edgeplatform (e.g., a node), a plan is made regarding which devices toconnect to the node. Usually additional device types cannot bedynamically connected because the node does not contain the softwareapplications to service the additional device type(s).

Specifically, in an IIoT environment, the right kind of analytic modelfor the situation or context of the node may not always be available onthe node. Since nodes may have limited resources, it may be difficult topersist all analytical models and, even if the node can persist allanalytical models, it would not be an efficient utilization ofresources. Further, if the node is offline or not connected to thecloud, the node may not have access to the appropriate analytical model(e.g., by requesting the appropriate analytical model from the cloud).Moreover, execution runtimes for the analytical models may vary between,for example, gateway devices and mobile devices.

Therefore, the analytical models trained in the cloud are sometimes notdirectly applicable at the edge node, unless the analytical models aresynchronized or transformed appropriately to get adapted to the edgeenvironment.

The present disclosure is directed to overcoming one or more of theseabove-referenced challenges.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, systems and methods aredisclosed for a contextual transformation of an analytical model for anindustrial internet of things (IIoT) edge node.

For instance, the method may include receiving the analytical model froma cloud service; obtaining local data of the IIoT edge node, the localdata including at least one of live stochastic data, asset models,super-system information, sub-system information, or performancemetrics; analyzing the local data to determine a situational context ofthe IIoT edge node, the situational context of the IIoT edge nodeincluding at least one of sensor context or domain context; determiningwhether to transform the analytical model based on a fit between theanalytical model and the situational context; and in response todetermining to transform the analytical model, transforming theanalytical model based on the situational context to derive atransformed analytical model.

Further, an industrial internet of things (IIoT) edge node, forcontextual transformation of an analytical model, may include a memorystoring instructions; and a processor executing the instructions toperform a process. The process including: receiving the analytical modelfrom a cloud service; obtaining local data of the IIoT edge node, thelocal data including at least one of live stochastic data, asset models,super-system information, sub-system information, or performancemetrics; analyzing the local data to determine a situational context ofthe IIoT edge node, the situational context of the IIoT edge nodeincluding at least one of sensor context or domain context; determiningwhether to transform the analytical model based on a fit between theanalytical model and the situational context; and in response todetermining to transform the analytical model, transforming theanalytical model based on the situational context to derive atransformed analytical model.

Moreover, a non-transitory computer-readable medium storing instructionsthat, when executed by an industrial internet of things (IIoT) edgenode, may cause the IIoT edge node to perform a method for contextualtransformation of an analytical model. The method including receivingthe analytical model from a cloud service; obtaining local data of theIIoT edge node, the local data including at least one of live stochasticdata, asset models, super-system information, sub-system information, orperformance metrics; analyzing the local data to determine a situationalcontext of the IIoT edge node, the situational context of the IIoT edgenode including at least one of sensor context or domain context;determining whether to transform the analytical model based on a fitbetween the analytical model and the situational context; and inresponse to determining to transform the analytical model, transformingthe analytical model based on the situational context to derive atransformed analytical model.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. As will beapparent from the embodiments below, an advantage to the disclosedsystems and methods is that multiple parties may fully utilize theirdata without allowing others to have direct access to raw data. Thedisclosed systems and methods discussed below may allow advertisers tounderstand users' online behaviors through the indirect use of raw dataand may maintain privacy of the users and the data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 illustrates components of an Industrial Internet of Things (IIoT)environment, according to one or more embodiments.

FIG. 2 illustrates an exemplary edge platform thru communicationsarchitecture layers of an Industrial Internet of Things (IIoT)environment, according to one or more embodiments.

FIG. 3 illustrates a diagram of a typical context analysis life cycle inthe IIoT system, according to one or more embodiments.

FIG. 4 illustrates a diagram of relationships between key parameterswhich influence a context of an IIoT Application which is running in anEdge Node, according to one or more embodiments.

FIG. 5 illustrates a block diagram for a contextual transformation of ananalytical model for an Industrial Internet of Things (IIoT) edge node,according to one or more embodiments.

FIG. 6 illustrates a system block diagram of an exemplary system for acontextual transformation of an analytical model for an industrialinternet of things (IIoT) edge node, according to one or moreembodiments.

FIG. 7 illustrates a flow chart of an exemplary method for a contextualtransformation of an analytical model for an industrial internet ofthings (IIoT) edge node, according to one or more embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of the present disclosure relate generally to anedge (e.g., edge nodes) of an industrial internet of things (IIoT)system and, more particularly, to systems and methods for contextualtransformation of an analytical model at the edge.

In general, the present disclosure is directed to the use of an edgenode to transform an analytical model based on the situational context.As described in more detail below, the edge node may analyze variousdata of the edge node and the environment in which the edge node isdeployed to identify the local context for executing the analyticalmodel, then the edge node may transform the analytical model, which wastrained in a cloud environment, based on the local context. It should beappreciated that the transformation of analytical models, as explainedin further detail below, is distinct from model tuning, which involveschanging certain parameter values to increase the accuracy of theanalytical model. On the other hand, transformation of analyticalmodels, as described herein, involves adapting or converting theanalytical model to run on multiple types of target edge nodeenvironments. Target edge nodes may have for instance varying CPU,memory, and storage footprints. Transformation does not change thefunctionality of the model algorithm but it enables execution of themodel in a specific target environment. Therefore, because an edge nodemay transform an analytical model, the model designer (e.g., a machinelearning-trained computer, artificial intelligence, and/or a datascientist) does not have to know the actual target environment in whichthe analytical model is intended to run.

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

As used herein, the terms “comprises,” “comprising,” “having,”including,” or other variations thereof, are intended to cover anon-exclusive inclusion such that a process, method, article, orapparatus that comprises a list of elements does not include only thoseelements, but may include other elements not expressly listed orinherent to such a process, method, article, or apparatus.

In this disclosure, relative terms, such as, for example, “about,”“substantially,” “generally,” and “approximately” are used to indicate apossible variation of ±10% in a stated value.

The term “exemplary” is used in the sense of “example” rather than“ideal.” As used herein, the singular forms “a,” “an,” and “the” includeplural reference unless the context dictates otherwise.

FIG. 1 illustrates components of an Industrial Internet of Things (IIoT)environment, according to one or more embodiments. As shown in FIG. 1,an IIoT environment may be organized in three primary layers (Cloud,Network, and the Edge).

The edge layer may include one or more sensors, actuators, or otherdevices 102, real time controllers 106, gateways 116, and ahuman-machine interface (HMI) 110. Real time controllers 106 may controlone or more smart network ready devices 130. The network layer (e.g. theInternet 120) may include any suitable wired or wireless network, suchas, for example, a local area network (LAN), wide area network (WAN),Ethernet, wireless fidelity (Wi-Fi), IEEE 802.11, Bluetooth or othershort-range radio communication, near field communication, or anycombination thereof. The cloud layer may include data and securitycommunications modules 105, modules for analytics 115, reporting 120,and planning 125, and a HMI 110.

An Edge Platform may comprise one or both of a gateway 116 and a realtime controller 106. Details of the Edge Platform are discussed below,for example in FIGS. 2 and 6.

FIG. 2 illustrates an exemplary edge platform as defined bycommunications architecture layers of an Industrial Internet of Things(IIoT) environment, according to one or more embodiments. As shown inFIG. 2, the Edge platform (edge node or IIoT edge node) may generallyinclude multiple layers, including a communications layer 210, anapplications layer 220, an operating system layer 230, and devicesinterface layer 240. Security and encryptions layer 260 may extend amongand between the plurality of layers of the edge node IIoT platform.

In one embodiment, the edge node software may include an operatingsystem layer 230 (with an operating system O/S, edged data platform fordata storage and management of data, and application support), asecurity and encryption module 260, a communications module 210, andinterfaces 240 for one or more connected devices. The applications layer220 of the edge node may contain applications from among a predeterminedset of edge applications, analytics, alarm management, actions, andbackup/recovery. Typically, a configuration of which devices willconnect to the node may be planned before deploying an edge node.Devices may connect to the edge node using protocols such as ZigBee,Z-Wave, BLE, OPC UA, BACNet, and the like.

The edge platform may be (1) at the low end, an embedded system, and (2)at the high end, a server class system. The edge platform infrastructuremay include a central processing unit (CPU), memory, storage, networkconnection(s) to the cloud (for example the Internet 120 of FIG. 1), andconnection(s) to controllers, devices and sensors (for example sensors,actuators, or other devices 102 of FIG. 1). The edge platform may runcommand and control applications, process stream and batch analytics,and send device generated telemetry data to the cloud. Similarly, dataoriginating in the cloud may be sent to the edge platform to beprocessed by (1) edge platform or (2) transmitted to various devices(for example sensors, actuators, or other devices 102). The edgeplatform may be a secure computing environment in which datacommunication between the edge platform to the cloud, as well as datacommunication between the edge platform and the various devices, isencrypted. The edge platform may also be called a node. Nodes aregenerally distributed closer to the various devices (in a hierarchalmanner), but do not necessarily have to be physically proximatelylocated to the various devices.

As mentioned above, the edge platform may receive data from the devicesand sensors (for example sensors, actuators, or other devices 102 ofFIG. 1). The received data may include two kinds of the data (1) dataregarding discrete events and data regarding continuous events. Discreteevents may be an event that occurs at time t and t+p, where there areconsidered to have been two separate event instances (e.g. a door open,lights on). Continuous events may be an event instance lasting for atleast time p, where an event occurring at time t and t+p, cannot beconsidered as two separate events (e.g. raining, having a shower,driving a car). Collectively, this data may be called “stochastic data.”

FIG. 3 illustrates a diagram of the typical context analysis life cyclein the IIoT system, according to one or more embodiments. The contextanalysis life cycle may express how edge data (e.g., from sensors,actuators, or other devices 102 of FIG. 1) is collected, modeled, andprocessed, and how knowledge is extracted from the collected data. Asshown in FIG. 3, the typical context analysis life cycle includescontext modelling 300, context reasoning 305, context distribution 310,and context acquisition 315. As shown in FIG. 2, context acquisition 315feeds into context modelling 300, context modelling 300 feeds intocontext reasoning 305, context reasoning 305 feeds into contextdistribution 310, and context distribution 310 feeds into contextacquisition 315, which uses new insights, if any, from extractedknowledge.

In context acquisition 315, the data is acquired from various physicaland virtual devices (e.g., from sensors, actuators, or other devices 102of FIG. 1). For instance, context acquisition 315 may includeinformation about an acquisition process, frequency, responsibility,sensor types, and/or source.

In context modeling 300, the data is modeled according to meaningfuldata. The modelling may be based on one or a combination of: (1) keyvalue modelling, (2) markup scheme modelling, (3) graphical modelling,(4) object oriented modelling, (5) logic based modelling, (6) ontologybased modelling, (7) spatial modelling, (8) uncertainty modelling, and(9) hybrid context modelling.

In context reasoning 305, the data is processed, and then knowledge isextracted. Processing and extracting may be based on one or acombination of: (1) fuzzy logic, (2) ontology based probabilistic logic,(3) rules, (4) supervised learning, and (5) unsupervised learning.

In context distribution 310, the extracted knowledge is distributed viaservers. For instance, the distribution may be accomplished by one or acombination of: querying and subscription.

FIG. 4 illustrates a diagram of relationships between key parameterswhich influence a context of an IIoT application which is running in anedge node, according to one or more embodiments.

Specifically, as shown in FIG. 4, diagram 400 illustrates contextmapping between different parameters of a node, where (P) denotes a rowlabel and (Q) denotes a column label, then: 1 means (P)∩(Q)≈very high; 2means (P)∩(Q)≈moderate; and 3 means (P)∩(Q)≈very low. Parameters mayinclude: user, computing (system), physical (environment), historical,social, networking, things, sensor, who (identify), where (location),when (time), what (activity), why, sensed, static, profiled, derived,operational, conceptual, objective, cognitive, external (physical),internal (logical), low-level (observable), and/or high level(non-observable). For instance, the intersection of sensor and physical(environment) is very high.

A node may obtain local data of the node (for instance at least one oflive stochastic data, asset models, super-system information, sub-systeminformation, or performance metrics, discussed below in relation to FIG.5). The node may analyze the local data to determine a situationalcontext of the node by determining a result for the parameters in FIG.4. For instance, the node may calculate a value (or values) based on thelocal data and the values in FIG. 4, and the value(s) may represent thesituational context of the node.

FIG. 5 illustrates a block diagram for a contextual transformation of ananalytical model for an industrial internet of things (IIoT) edge node,according to one or more embodiments.

A model transformer 505 may receive (1) a serialized model graph 500 aof an original model 500 (an “analytical model”) and (2) a situationalcontext from a content analyzer 510. Context analyzer 510 may receive(1) sensor context 510 a and (2) domain context 510 b, and output thesituational context to the model transformer 505.

Specifically, the context analyzer 510 may determine the situationalcontext by correlating the stochastic data discussed above and one ormore of (1) ontology information/asset model, (2) super systeminformation, (3) sub system information, (4) performance metrics, and(5) other physical/logical dimensions. Ontology information/asset modelmay include information about a hierarchy of devices in a specificdomain, for instance information about one or more of: how devices areconnected to another device, how the devices form a family, and how thedevices communicate with each other. Super system information mayinclude information about how a specific node (as deployed) is connectedor interacts with other devices and what, if any, other nodes are in thesame environment, for instance information about one or more of: devicesconnected below the specific node, what router/gateway/localnetwork/wide area network the specific node communicates with to connectto the cloud, and other nodes in the same environment operating parallelto the specific node. Sub system information may include informationabout a configuration of the specific node (as deployed), for instanceinformation about one or more of: number of processors and type(s),number of data storages and type(s), other hardware information of thespecific node, and software runtime of the node including behaviorparameters of the node. Performance metrics may include one or more ofresponse time, throughput, computing resources, bandwidth, latency, andpower consumption.

Sensor context 510 a includes data from the above data and informationrelative to the sensors and devices (e.g., sensors, actuators, or otherdevices 102 of FIG. 1). Domain context 510 b includes data from theabove data and information relative to the edge platform.

After the model transformer 505 determines that the original context ofthe original model 500 does not match the desired context of the IIoTedge node (i.e., the original context of the analytical model does notfit the situational context of the IIoT edge node), the modeltransformer 505 transforms the original model by outputting informationto the model compiler 515. The model compiler 515 outputs a transformedmodel 520, with a serialized model graph 520 a. This transformation maymake the transformed model 520 work in new hardware environments, thusproviding hardware agnostic support for analytical models. For instance,when the domain context indicates that the edge node is a small platformdevice, the transformation may transform the analytical model to reducememory and storage requirements for the analytical model; when thedomain context indicates that rapid processing is required (e.g.,because of safety concerns), the transformation may transform theanalytical model to reduce latency.

An example process may include: (1) Export an original model 500(existing analytical model) into a serialized model graph 500 a. Forexample, to create a serialized model graph 500 a, the original model500 may load the graph definition of the original model 500, pull invalues for all of the variables from a latest checkpoint file, and thenreplace each variable OP with a constant that has the numerical data forthe weights stored in its attributes, then strip away all the extraneousnodes of the graph definition that are not used for forward inference,and save out the resulting graph definition into an output file. Theresulting graph definition may include (1) nodes, (2) names, (3)operations (OPs), (4) inputs, (5) devices, and/or (5) attributes.Definition of weights, bias and checkpoints may remain intact in theresulting graph definition.

(2) The model compiler will apply and compile the above-mentionedparameters to match with the desired context based upon the meta datathat is available.

For instance, the transformation process may perform one or sequentialcombinations of: (1) remove unused nodes; (2) collapse sub-graphs thatevaluate to constant expressions to a constant; (3) add defaultattributes to operations; (4) converting newer versions of operations toolder versions (or vice versa); (5) optimize away a Mul that isintroduced after a Conv2D (or a MatMul) when batch normalization hasbeen used during training; (6) quantize calculations from floating-pointto eight-bit (or vice versa); (7) fusing specific combinations ofpatterns of operations to improve memory usage and latency (e.g.,ResizeBilinear or MirrorPad ops before convolutions by fusing thespatial transformations with the convolution's im2col patch generation);(8) merging duplicate nodes; (9) quantize nodes; (10) quantize weights;(11) remove specified devices from operations; (12) remove controldependencies; (13) round all float values; and (14) change gatheroperations to hash table operations.

Additionally, the transformation may also enable one or more of: (1)mapping and translation of analytical model notations from onelanguage/technology to another; (2) Converting the Physical structure ofthe model to another in order to get executed/compatible with a newhardware/AI processor environment and became hardware agnostic; (3)mapping and translation of analytical models for a particular domainfrom one language/format to another; (4) mapping and translation oneXML/frozen graph (or other) format to another for an analytical model;(5) mapping and translation of meta-models; and (6) supportingbi-directional consistency management between complex models.

FIG. 6 illustrates a system block diagram of an exemplary system for anindustrial internet of things (IIoT) edge node for contextualtransformation of an analytical model for, according to one or moreembodiments.

As shown in FIG. 6, the exemplary system may have three layers (1) dataingress/egress layer, (2) applications and analytics models layer, and(3) IoT platform layer. Specifically, the exemplary system may includeedge node 600, LAN or WAN 630, IoT platform services 640, and browser650 (accessible and controllable by user(s) 660).

IoT platform services 640 may include model management service 640 a,analytics model 640 b, and node manager 640 c. Analytical models may bestored as analytics model 640 b, which may be stored in a memory in theIoT platform services 640. Further, IoT platform services 640 mayprovide various services including IIoT services such as devicemanagement, device provisioning, data services, etc. Data scientistsdevelop analytical models and store the analytical models in memory inthe IoT platform service 640, the analytical models being based on thedata received from various edge nodes, like edge node 600. The modelmanagement service 640 a may be responsible for pushing the analyticalmodel 640 b to the edge nodes, like edge node 600. Further, the nodemanager 640 c monitors the health of the edge nodes, like edge node 600.The node manager 640 c also provides a user interface (accessible bybrowser 650) to the user(s) 660 so that user(s) 660 may define the rulesand configurations that are downloaded into the edge nodes, like edgenode 600.

Edge node 600 may include infrastructure/operating system 605, dataingest/egress 610, data bus 615, applications and analytics models 620.

Data ingest/egress 610 may include a data bus adapter 610 b and variousbrokers, clients, agents, and drivers 610 a that communicate withvarious devices and sensors (e.g., sensors, actuators, or other devices102 of FIG. 1). Brokers, clients, agents, and drivers 610 a may includea Message Queuing Telemetry Transport (MQTT) broker, OPC/UCA client,ModBus Agent, BACnet Driver, or custom protocol driver. Data bus adapter610 b may collect data from brokers, clients, agents, and drivers 610 aand transmit it to the data bus 615 (and vice versa: data from data bus615 to brokers, clients, agents, and drivers 610 a).

Applications and analytics models 620 may include rules engine 620 a,cloud connector 620 b, analytics model 620 c, node agent 620 d,analytics model transformer 620 e, and data manager 620 f. Theapplications and analytics models 620 may be executed by one or moreprocessors that are connected to the data bus 615. Further, the one ormore processors or the date bus 615 may be connected to a memory thatmay store the various instructions, rules, analytical models, and datafrom the various devices and sensors or data from the IoT platformservice 640. For instance, the memory may store some data in a TimeSeries Database (TSDB) that is controlled by the data manager 620 f.

Further, the applications and analytics models 620 include theapplications that process data sent by the sensors/devices, for instancethe rules engine 620 a and analytics model 620 c. The rules engine 620 amay be used to apply rules defined by the user to incoming data. Thecloud connector 620 b is the bridge that connects the edge node 600 tothe IoT platform service 640 in the cloud, by the LAN or WAN 630. Thenode agent 620 d communicates with the node manager 640 c in the IoTplatform service 640 in the cloud, by the LAN or WAN 630, and brings therules of the rules engine 620 a as well as configuration data from theIoT platform service 640 in the cloud to the edge node 600. The datamanager 620 f handles all the data that needs to be storedtemporarily/permanently and locally on the edge node 600.

All the components in the applications and analytics models 620 aredepicted as communicating over the data bus 615. However, this is merelya depiction as the communications between the different applications maybe accomplished (1) by processing sequences on a single processor, (2)by flags in memory for various processing sequences on a singleprocessor or different processors, or (3) by signals between thedifferent processors.

Based on the local context, the analytics model transformer 620 etransforms the analytical model received from the IoT platform service640 in the cloud into a transformed analytical model (for instance, asdiscussed above with respect to FIG. 5).

Example (See the numbered lines in FIG. 6 for reference): (1) a datascientist develops an analytical model and stores it in the AnalyticsModel 640 b, and makes the analytical model available to the modelmanagement service 640 a for distribution to the edge node(s), like edgenode 600. (2) The model management service 640 a may push the analyticalmodel down to the edge node 600 through the LAN or WAN 630 and to thecloud connector 620 b of the edge node 600. (3) The cloud connector 620b may publish the “Analytical Model is available” event on the data bus615. (4) The analytics model transformer 602 e, which has subscribed tothe “Analytical Model is available” event, may receive the event andthereby receive the analytical model. (5) The analytics modeltransformer 620 e transforms the analytical model based on the localcontext, and pushes the transformed analytical model to the analyticsmodel 620 c so that the analytics model 620 c (now transformed based oncontext) may start processing new data to determine outputs.

In another embodiment, the edge node 600 may monitor the local contextof the edge node 600 (e.g., after the edge node 600 first transforms theanalytical model). In response to detecting a change in the localcontext, the edge node 600 may transform the analytical model based onthe changed local context.

FIG. 7 illustrates a flow chart of an exemplary method for a contextualtransformation of an analytical model to be executed an industrialinternet of things (IIoT) edge node, according to one or moreembodiments. The method may include: receiving an analytical model froma cloud service (block 700); obtaining local data of an IIoT edge node,the local data including at least one of live stochastic data, assetmodels, super-system information, sub-system information, or performancemetrics (block 702); analyzing the local data to determine a situationalcontext of the IIoT edge node, the situational context of the IIoT edgenode including at least one of sensor context or domain context (block704); determining whether to transform the analytical model based on afit between the analytical model and the situational context (block706); in response to determining to transform the analytical model,transforming the analytical model based on the situational context toderive a transformed analytical model (block 708).

FIGS. 1 through 7 and the disclosure provide a brief, generaldescription of a suitable computing environment in which the presentdisclosure may be implemented. In one embodiment, any of the disclosedsystems, methods, and/or graphical user interfaces may be executed by orimplemented by a computing system consistent with or similar to thatdepicted in FIGS. 1 through 7. Although not required, aspects of thepresent disclosure are described in the context of computer-executableinstructions, such as routines executed by a data processing device,e.g., a server computer, wireless device, and/or personal computer.Those skilled in the relevant art will appreciate that aspects of thepresent disclosure can be practiced with other communications, dataprocessing, or computer system configurations, including: Internetappliances, hand-held devices (including personal digital assistants(“PDAs”)), wearable computers, all manner of cellular or mobile phones(including Voice over IP (“VoIP”) phones), dumb terminals, mediaplayers, gaming devices, virtual reality devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,set-top boxes, network PCs, mini-computers, mainframe computers, and thelike. Indeed, the terms “computer,” “server,” and the like, aregenerally used interchangeably herein, and refer to any of the abovedevices and systems, as well as any data processor.

Aspects of the present disclosure may be embodied in a special purposecomputer and/or data processor that is specifically programmed,configured, and/or constructed to perform one or more of thecomputer-executable instructions explained in detail herein. Whileaspects of the present disclosure, such as certain functions, aredescribed as being performed exclusively on a single device, the presentdisclosure may also be practiced in distributed environments wherefunctions or modules are shared among disparate processing devices,which are linked through a communications network, such as a Local AreaNetwork (“LAN”), Wide Area Network (“WAN”), and/or the Internet.Similarly, techniques presented herein as involving multiple devices maybe implemented in a single device. In a distributed computingenvironment, program modules may be located in both local and/or remotememory storage devices.

Aspects of the present disclosure may be stored and/or distributed onnon-transitory computer-readable media, including magnetically oroptically readable computer discs, hard-wired or preprogrammed chips(e.g., EEPROM semiconductor chips), nanotechnology memory, biologicalmemory, or other data storage media. Alternatively, computer implementedinstructions, data structures, screen displays, and other data underaspects of the present disclosure may be distributed over the Internetand/or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, and/or they may beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme).

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

What is claimed is:
 1. A method for a contextual transformation of ananalytical model for an industrial internet of things (IIoT) edge node,comprising: receiving the analytical model from a cloud service;obtaining local data of the IIoT edge node; analyzing the local data todetermine a situational context of the IIoT edge node; determiningwhether to transform the analytical model based on a fit between theanalytical model and the situational context; and in response todetermining to transform the analytical model, transforming theanalytical model based on the situational context to derive atransformed analytical model.
 2. The method of claim 1, wherein thelocal data includes at least one of live stochastic data, asset models,super-system information, sub-system information, or performancemetrics.
 3. The method of claim 1, wherein the situational context ofthe IIoT edge node includes at least one of sensor context or domaincontext.
 4. The method of claim 1, further comprising: monitoring thesituational context for changes; and in response to detecting a changeof the situational context as indicated by the monitoring, transformingthe transformed analytical model based on the change of the situationalcontext.
 5. The method of claim 1, further comprising: obtainingstochastic data; and applying the transformed analytical model to thestochastic data to determine outputs.
 6. The method of claim 1, whereinthe transforming the analytical model includes performing one or asequential combination of: converting newer versions of operations toolder versions operations; optimizing operations introduced duringtraining; quantizing calculations from floating-point to eight-bit; andfusing specific combinations of patterns of operations.
 7. The method ofclaim 1, wherein the performing the one or the sequential combinationincludes: removing unused nodes; collapsing sub-graphs that evaluate toconstant expressions to a constant; adding default attributes tooperations; merging duplicate nodes; quantizing nodes or weights;removing specified devices from operations; removing controldependencies; rounding all float values; and changing gather operationsto hash table operations.
 8. An industrial internet of things (IIoT)edge node for contextual transformation of an analytical model, the IIoTedge node comprising: a memory storing instructions; and a processorexecuting the instructions to perform a process including: receiving theanalytical model from a cloud service; obtaining local data of the IIoTedge node; analyzing the local data to determine a situational contextof the IIoT edge node; determining whether to transform the analyticalmodel based on a fit between the analytical model and the situationalcontext; and in response to determining to transform the analyticalmodel, transforming the analytical model based on the situationalcontext to derive a transformed analytical model.
 9. The IIoT edge nodeof claim 8, wherein the local data includes at least one of livestochastic data, asset models, super-system information, sub-systeminformation, or performance metrics.
 10. The IIoT edge node of claim 8,wherein the situational context of the IIoT edge node includes at leastone of sensor context or domain context
 11. The IIoT edge node of claim8, wherein the process performed by the processor further comprises:monitoring the situational context for changes; and in response todetecting a change of the situational context as indicated by themonitoring, transforming the transformed analytical model based on thechange of the situational context.
 12. The IIoT edge node of claim 8,wherein the process performed by the processor further comprises:obtaining stochastic data; and applying the transformed analytical modelto the stochastic data to determine outputs.
 13. The IIoT edge node ofclaim 8, wherein the transforming the analytical model includesperforming one or a sequential combination of: converting newer versionsof operations to older versions operations; optimizing operationsintroduced during training; quantizing calculations from floating-pointto eight-bit; and fusing specific combinations of patterns ofoperations.
 14. The IIoT edge node of claim 13, wherein the performingthe one or the sequential combination includes: removing unused nodes;collapsing sub-graphs that evaluate to constant expressions to aconstant; adding default attributes to operations; merging duplicatenodes; quantizing nodes or weights; removing specified devices fromoperations; removing control dependencies; rounding all float values;and changing gather operations to hash table operations.
 15. Anon-transitory computer-readable medium storing instructions that, whenexecuted by an industrial internet of things (IIoT) edge node, cause theIIoT edge node to perform a method for contextual transformation of ananalytical model, the method comprising: receiving the analytical modelfrom a cloud service; obtaining local data of the IIoT edge node;analyzing the local data to determine a situational context of the IIoTedge node; determining whether to transform the analytical model basedon a fit between the analytical model and the situational context; andin response to determining to transform the analytical model,transforming the analytical model based on the situational context toderive a transformed analytical model.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the local data includes atleast one of live stochastic data, asset models, super-systeminformation, sub-system information, or performance metrics.
 17. Thenon-transitory computer-readable medium of claim 15, wherein thesituational context of the IIoT edge node includes at least one ofsensor context or domain context.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the method furthercomprises: monitoring the situational context for changes; and inresponse to detecting a change of the situational context as indicatedby the monitoring, transforming the transformed analytical model basedon the change of the situational context.
 19. The non-transitorycomputer-readable medium of claim 15, wherein the method furthercomprises: obtaining stochastic data; and applying the transformedanalytical model to the stochastic data to determine outputs.
 20. Thenon-transitory computer-readable medium of claim 19, wherein thetransforming the analytical model includes performing one or asequential combination of: converting newer versions of operations toolder versions operations; optimizing operations introduced duringtraining; quantizing calculations from floating-point to eight-bit; andfusing specific combinations of patterns of operations.